Predict gram - positive and gram - negative subcellular localization via incorporating evolutionary information and physicochemical features into Chou’s general PseAAC

In this study, we used structural and evolutionary based features to represent the sequences of gram-positive and gram-negative subcellular localizations. To do this, we proposed a normalization method to construct a normalize Position Specific Scoring Matrix (PSSM) using the information from original PSSM. To investigate the effectiveness of the proposed method we compute feature vectors from normalize PSSM and by applying Support Vector Machine (SVM) and Naïve Bayes classifier, respectively, we compared achieved results with the previously reported results. We also computed features from original PSSM and normalized PSSM and compared their results. The archived results show enhancement in gram-positive and gram-negative subcellular localizations. Evaluating localization for each feature, our results indicate that employing SVM and concatenating features (amino acid composition feature, Dubchak feature (physicochemical-based features), normalized PSSM based auto-covariance feature and normalized PSSM based bigram feature) have higher accuracy while employing Naïve Bayes classifier with normalized PSSM based auto-covariance feature proves to have high sensitivity for both benchmarks. Our reported results in terms of overall locative accuracy is 84.8% and overall absolute accuracy is 85.16% for gram-positive dataset; and, for gram- negative dataset, overall locative accuracy is 85.4% and overall absolute accuracy is 86.3%

APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility

<p>Abstract</p> <p>Background</p> <p>It is well known that most of the binding free energy of protein interaction is contributed by a few key hot spot residues. These residues are crucial for understanding the function of proteins and studying their interactions. Experimental hot spots detection methods such as alanine scanning mutagenesis are not applicable on a large scale since they are time consuming and expensive. Therefore, reliable and efficient computational methods for identifying hot spots are greatly desired and urgently required.</p> <p>Results</p> <p>In this work, we introduce an efficient approach that uses support vector machine (SVM) to predict hot spot residues in protein interfaces. We systematically investigate a wide variety of 62 features from a combination of protein sequence and structure information. Then, to remove redundant and irrelevant features and improve the prediction performance, feature selection is employed using the F-score method. Based on the selected features, nine individual-feature based predictors are developed to identify hot spots using SVMs. Furthermore, a new ensemble classifier, namely APIS (A combined model based on Protrusion Index and Solvent accessibility), is developed to further improve the prediction accuracy. The results on two benchmark datasets, ASEdb and BID, show that this proposed method yields significantly better prediction accuracy than those previously published in the literature. In addition, we also demonstrate the predictive power of our proposed method by modelling two protein complexes: the calmodulin/myosin light chain kinase complex and the heat shock locus gene products U and V complex, which indicate that our method can identify more hot spots in these two complexes compared with other state-of-the-art methods.</p> <p>Conclusion</p> <p>We have developed an accurate prediction model for hot spot residues, given the structure of a protein complex. A major contribution of this study is to propose several new features based on the protrusion index of amino acid residues, which has been shown to significantly improve the prediction performance of hot spots. Moreover, we identify a compact and useful feature subset that has an important implication for identifying hot spot residues. Our results indicate that these features are more effective than the conventional evolutionary conservation, pairwise residue potentials and other traditional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spot residues. The data and source code are available on web site <url>http://home.ustc.edu.cn/~jfxia/hotspot.html</url>.</p